Nonparametric estimation and inference for panel data models

Christopher F. Parmeter; Jeffrey S. Racine

doi:10.1016/B978-0-12-814367-4.00004-6

Nonparametric estimation and inference for panel data models

Christopher F. Parmeter, Jeffrey S. Racine

Economics

Research output: Chapter in Book/Report/Conference proceeding › Chapter

2 Scopus citations

Abstract

This chapter surveys nonparametric methods for estimation and inference in a panel data setting. Methods surveyed include profile likelihood, kernel smoothers, as well as series and sieve estimators. The practical application of nonparametric panel-based techniques is less prevalent than, for example, say, nonparametric density and regression techniques. It is our hope that the material covered in this chapter will prove useful and facilitate its adoption by practitioners.

Original language	English (US)
Title of host publication	Panel Data Econometrics
Subtitle of host publication	Theory
Publisher	Elsevier
Pages	97-129
Number of pages	33
ISBN (Electronic)	9780128143674
ISBN (Print)	9780128144312
DOIs	https://doi.org/10.1016/B978-0-12-814367-4.00004-6
State	Published - Jan 1 2019

Keywords

Data generating process
Generalized least squares
Nonparametric estimations
One-way error component model
Panel data models models
Random effects

ASJC Scopus subject areas

Economics, Econometrics and Finance(all)
Business, Management and Accounting(all)

Access to Document

10.1016/B978-0-12-814367-4.00004-6

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